Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0677930 (primary tumor)
 20,210 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Deregulation of microRNA (miRNA) expression can have a critical role in carcinogenesis. Here we show in prostate cancer that miRNA-205 (miR-205) transcription is commonly repressed and the MIR-205 locus is hypermethylated. LOC642587, the MIR-205 host gene of unknown function, is also concordantly inactivated. We show that miR-205 targets mediator 1 (MED1, also called TRAP220 and PPARBP) for transcriptional silencing in normal prostate cells, leading to reduction in MED1 mRNA levels, and in total and active phospho-MED1 protein. Overexpression of miR-205 in prostate cancer cells negatively affects cell viability, consistent with a tumor suppressor function. We found that hypermethylation of the MIR-205 locus was strongly related with a decrease in miR-205 expression and an increase in MED1 expression in primary tumor samples (n=14), when compared with matched normal prostate (n=7). An expanded patient cohort (tumor n=149, matched normal n=30) also showed significant MIR-205 DNA methylation in tumors compared with normal, and MIR-205 hypermethylation is significantly associated with biochemical recurrence (hazard ratio=2.005, 95% confidence interval (1.109, 3.625), P=0.02), in patients with low preoperative prostate specific antigen. In summary, these results suggest that miR-205 is an epigenetically regulated tumor suppressor that targets MED1 and may provide a potential biomarker in prostate cancer management.
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PMID:Epigenetic-induced repression of microRNA-205 is associated with MED1 activation and a poorer prognosis in localized prostate cancer. 2286 46

The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP ( http://webs.iiitd.edu.in/raghava/cancerspp/ ).
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PMID:Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. 3167 75